Deep learning model to predict fracture mechanisms of graphene
Name
s41699-021-00228-x.pdf
Size
1.53 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Lew, Andrew James
Yu, Chi-Hua
Hsu, Yu-Chuan
Buehler, Markus J
Date Issued
April 2021
Journal
npj 2D Materials and Applications
Publisher
Springer Science and Business Media LLC
Citation
Lew, Andrew J. et al. "Deep learning model to predict fracture mechanisms of graphene." npj 2D Materials and Applications 5, 1 (April 2021): 48. © 2021 The Author(s)
Version
Final published version
Abstract
Understanding fracture is critical to the design of resilient nanomaterials. Molecular dynamics offers a way to study fracture at an atomistic level, but is computationally expensive with limitations of scalability. In this work, we build upon machine-learning approaches for predicting nanoscopic fracture mechanisms including crack instabilities and branching as a function of crystal orientation. We focus on a particular technologically relevant material system, graphene, and apply a deep learning method to the study of such nanomaterials and explore the parameter space necessary for calibrating machine-learning predictions to meaningful results. Our results validate the ability of deep learning methods to quantitatively capture graphene fracture behavior, including its fractal dimension as a function of crystal orientation, and provide promise toward the wider application of deep learning to materials design, opening the potential for other 2D materials.
MIT Department
Massachusetts Institute of Technology. Laboratory for Atomistic and Molecular Mechanics
Massachusetts Institute of Technology. Department of Chemistry
Massachusetts Institute of Technology. Center for Computational Science and Engineering
Massachusetts Institute of Technology. Center for Materials Science and Engineering
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/s41699-021-00228-x
